U.S. patent number 10,265,543 [Application Number 14/358,072] was granted by the patent office on 2019-04-23 for beam segment-level dose computation and temporal motion tracking for adaptive treatment planning.
This patent grant is currently assigned to KONINKLIJKE PHILIPS N.V., WASHINGTON UNIVERSITY. The grantee listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Shyam Bharat, Karl Antonin Bzdusek, Parag Jitendra Parikh, Mingyao Zhu.
United States Patent |
10,265,543 |
Bharat , et al. |
April 23, 2019 |
Beam segment-level dose computation and temporal motion tracking
for adaptive treatment planning
Abstract
A treatment planning system for generating patient-specific
treatment. The system including one or more processors programmed
to receive a radiation treatment plan (RTP) for irradiating a
target over the course of one or more treatment fractions, said RTP
including a planned dose distribution to be delivered to the
target, receive motion data for at least one of the treatment
fractions of the RTP, receive temporal delivery metric data for at
least one of the treatment fractions of the RTP, calculate a
motion-compensated dose distribution for the target using the
motion data and the temporal delivery metric data to adjust the
planned dose distribution based on the received motion data and
temporal delivery metric data, and compare the motion-compensated
dose distribution to the planned dose distribution.
Inventors: |
Bharat; Shyam (Ossining,
NY), Zhu; Mingyao (Manchester, MO), Parikh; Parag
Jitendra (Webster Groves, MO), Bzdusek; Karl Antonin
(Madison, WI) |
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
N/A |
NL |
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Assignee: |
KONINKLIJKE PHILIPS N.V.
(Eindhoven, NL)
WASHINGTON UNIVERSITY (St. Louis, MS)
|
Family
ID: |
47429993 |
Appl.
No.: |
14/358,072 |
Filed: |
November 30, 2012 |
PCT
Filed: |
November 30, 2012 |
PCT No.: |
PCT/IB2012/056867 |
371(c)(1),(2),(4) Date: |
May 14, 2014 |
PCT
Pub. No.: |
WO2013/080175 |
PCT
Pub. Date: |
June 06, 2013 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20140336438 A1 |
Nov 13, 2014 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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61564885 |
Nov 30, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B
18/00 (20130101); A61N 5/1031 (20130101); A61N
5/1071 (20130101); A61N 5/1039 (20130101); A61N
7/02 (20130101); A61N 5/1064 (20130101); A61N
5/1037 (20130101); A61N 5/1077 (20130101); A61N
5/1047 (20130101); A61N 2005/1087 (20130101) |
Current International
Class: |
A61N
5/10 (20060101); A61N 7/02 (20060101); A61B
18/00 (20060101) |
Field of
Search: |
;600/1 ;378/65 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2108401 |
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Oct 2009 |
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EP |
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2011502010 |
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Jan 2011 |
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JP |
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2010109345 |
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Sep 2010 |
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WO |
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Other References
Hugo, G. D., et al.; Population and patient-specific target margins
for 4D adaptive radiotherapy to account for intra- and
inter-fraction variation in lung tumour position; 2007; Phys. Med.
Biol.; 52:257-274. cited by applicant.
|
Primary Examiner: Gilbert; Samuel G
Government Interests
This invention was made with government support under grant
CA134541 awarded by the National Institutes of Health. The
government has certain rights in the invention.
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a national filing of PCT application Serial No.
PCT/IB2012/056867, filed Nov. 30, 2012, published as WO 2013/080175
A1 on Jun. 6, 2013, which claims the benefit of U.S. provisional
application Ser. No. 61/564,885 filed Nov. 30, 2011, which is
incorporated herein by reference.
Claims
Having thus described the preferred embodiments, the invention is
now claimed to be:
1. A radiation therapy treatment planning system, comprising: one
or more processors; and a non-transitory storage medium storing
executable instructions programming the one or more processors to:
receive a radiation treatment plan (RTP) for irradiating a target
over a course of one or more treatment fractions, said RTP
including a planned dose distribution to be delivered to the
target; receive motion data of at least a part of a patient for at
least one of the treatment fractions of the RTP performed by a
treatment delivery apparatus and temporal delivery metric data
received from the treatment delivery apparatus which details the
status of each segment of each beam at each time instant of
radiation delivery; receive temporal delivery metric data for at
least one of the treatment fractions of the RTP; generate estimated
dose grids for each beam from the motion data and the temporal
delivery metric data; calculate a motion-compensated dose
distribution for the target by summing each of the estimated dose
grids; compare the motion-compensated dose distribution to the
planned dose distribution; adjust the planned dose distribution
based on the comparison to generate an adjusted RTP including the
adjusted planned dose distribution; and store the adjusted RTP in a
radiation therapy plan memory for use in controlling a radiation
therapy apparatus to deliver the adjusted planned dose distribution
to the target.
2. The radiation therapy treatment planning system according to
claim 1, wherein the temporal delivery metric data includes at
least one angular position of a gantry at all times during that
fraction and a number of segments belonging to a particular beam
that is active at any given time instant.
3. The radiation therapy treatment planning system according to
claim 1, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to: optimize the RTP for one of external beam radiation
therapy, proton therapy, ablation therapy and high-intensity
focused ultrasound therapy.
4. The radiation therapy treatment planning system according to
claim 1, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to: calculate the planned dose distribution from each
segment of the beam; and correlate the calculated planned dose
distribution with the motion data to estimate the
motion-compensated dose distribution received by the target.
5. The radiation therapy treatment planning system according to
claim 1, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to: control a radiation therapy device comprising a linear
accelerator by operating a multi-leaf collimator of the linear
accelerator to perform beam modulation as the linear accelerator is
moved or stepped in accordance with the adjusted RTP stored in the
radiation therapy plan memory to deliver the adjusted planned dose
distribution to the target.
6. The radiation therapy treatment planning system according to
claim 5, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to perform the adjusting after one or more treatment
fractions.
7. The radiation therapy treatment planning system according to
claim 5, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to perform the adjusting during a fraction.
8. The radiation therapy treatment planning system according to
claim 1, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to calculate a motion-compensated dose distribution for the
target using the motion data and the temporal delivery metric data
by: creating one or more probability density functions (PDFs) from
the motion data and temporal delivery metric data, each of said
PDFs representing a cumulative motion and delivery pattern of a
target or an organ during one or more treatment fractions or any
other period that motion data is collected.
9. The radiation therapy treatment planning system according to
claim 8, wherein the one or more processors are further programmed
by the executable instructions stored on the non-transitory storage
medium to calculate the motion-compensated dose distribution for
the target by: convolving the planned dose distributions with the
PDFs to determine the estimated dose grids indicative of a dose
actually delivered to the target.
10. The radiation therapy treatment planning system according to
claim 1, further including: a display; wherein the one or more
processors are further programmed by the executable instructions
stored on the non-transitory storage medium to compare the
motion-compensated dose distribution to the planned dose
distribution by at least one of: displaying the motion-compensated
dose distribution adjacent to the planned dose distribution on the
display; and, displaying the motion-compensated dose distribution
overlaid on the planned dose distribution.
11. A radiation therapy system comprising: one or more imaging
modalities, comprising at least one of a computed tomography (CT)
scanner, a positron emission tomography (PET) scanner, a magnetic
resonance (MR) scanner, or a single photon emission computed
tomography (SPECT) scanner, that obtain one or more planning
images, the planning images being diagnostic images of a region of
a subject to be treated; a radiation therapy treatment planning
system according to claim 1 that generates a radiation treatment
plan (RTP) for irradiating a target over a course of one or more
treatment fractions from the obtained one or more planning images,
said RTP including a planned dose distribution for the target; a
radiation therapy apparatus including a linear accelerator (LINAC)
with a multi-leaf collimator configured to deliver radiation
therapy in accordance with the RTP; a dose delivery monitor
generating the received temporal delivery metric data indicative of
the temporal delivery metrics of the radiation therapy apparatus;
and, a motion monitor generating the motion data from target
surrogates of the target; wherein the one or more processors of the
radiation therapy treatment planning system are further programmed
by the executable instructions stored on the non-transitory storage
medium of the radiation therapy treatment planning system to:
adjust the RTP based on the motion data; and control the radiation
therapy device to deliver the adjusted RTP to the patient by
operating the multi-leaf collimator to perform beam modulation as
the LINAC is moved or stepped.
12. A radiation therapy system comprising: a radiation therapy
apparatus comprising a linear accelerator (LINAC) with a multi-leaf
collimator (MLC), the radiation therapy apparatus configured to
deliver radiation therapy in accordance with a radiation treatment
plan (RTP); a motion monitor generating motion data from target
surrogates of a target; a dose delivery monitor generating temporal
delivery metric data indicative of one or more temporal delivery
metrics of the radiation therapy apparatus; and, one or more
processors programmed to: receive the RTP for irradiating a target
over a course of one or more treatment fractions, said RTP
including a planned dose distribution for the target; receive the
motion data for at least one of the treatment fractions of the RTP;
receive the temporal delivery metric data for at least one of the
treatment fractions of the RTP; generate estimated dose grids for
each beam from the motion data and the temporal delivery metric
data; calculate a motion-compensated dose distribution for the
target by summing each of the generated dose grids; compare the
motion-compensated dose distribution to the planned dose
distribution; adjust the RTP based on the compared
motion-compensated dose distribution to the planned dose
distribution; and control the radiation therapy apparatus including
operating the MLC to perform beam modulation as the LINAC is moved
or stepped to deliver radiation according to the adjusted RTP.
13. A method for generating a radiation therapy treatment plan,
said method comprising: receiving a radiation treatment plan (RTP)
for irradiating a target over a course of one or more treatment
fractions, said RTP including a planned dose distribution for the
target; receiving motion data of at least a part of a patient for
at least one of the treatment fractions of the RTP; receiving
temporal delivery metric data for at least one of the treatment
fractions of the RTP; generating estimated dose grids for each beam
from the motion data and the temporal delivery metric data;
calculating a motion-compensated dose distribution for the target
using the motion data and temporal delivery metric data to adjust
the planned dose distribution by summing each of the generated dose
grids; and, comparing the motion-compensated dose distribution to
the planned dose distribution; generating an adjusted RTP from the
comparison between the motion-compensated dose distribution to the
planned dose distribution; storing the adjusted RTP in a radiation
therapy plan memory; and controlling a radiation therapy device
with the adjusted RTP.
14. The method according to claim 13, wherein the generating of the
adjusted RTP includes: adjusting the generated radiation treatment
plan based on dosimetric differences between the motion-compensated
dose distribution and the planned dose distribution.
15. The method according to claim 13, further including: generating
estimated dose grids for each beam from the motion data and
temporal delivery metric data; and calculating the
motion-compensated dose distributions by summing each of the
estimated dose grids.
16. The method according to claim 13, wherein the calculating
includes: creating one or more probability density functions (PDFs)
from the motion data and the temporal delivery metric data, each of
said PDFs representing a cumulative motion pattern of a target or
an organ during the one or more treatment fractions or any other
period that the motion data is collected; and, convolving the
planned dose distributions with the PDFs to determine one or more
motion-compensated doses indicative of a dose actually delivered to
the target.
17. The method according to claim 16, wherein the calculating
further includes: accumulating the motion-compensated doses of at
least one of the treatment fractions.
18. The method according to claim 13, wherein the comparing
includes at least one of: displaying the motion-compensated dose
distribution adjacent to the planned dose distribution; displaying
the motion-compensated dose distribution overlaid on the planned
dose distribution; and, calculating a difference between the
motion-compensated dose distribution and the planned dose
distribution.
Description
The present application relates generally to external beam
radiation therapy (EBRT). It finds particular application in
conjunction with individual beam segment-level dose computation and
temporal motion tracking for adaptive treatment planning in
external beam radiation therapy, and will be described with
particular reference thereto. However, it is to be understood that
it also finds application in other usage scenarios, and is not
necessarily limited to the aforementioned application.
In external beam radiation therapy (EBRT), spatially targeted doses
of radiation are applied to tumors or other targets containing
cancerous or malignant tissue. Growing and rapidly multiplying
cancer cells tend to be more susceptible to damage from radiation,
as compared with normal cells, such that dosages administrated by
proper planning preferentially kill cancerous or malignant tissue.
Traditionally, EBRT consists of three stages: simulation (imaging),
planning, and delivery, in that order. The treatment planning is
usually performed using Computed Tomography (CT) images obtained
apriori. The radiation delivery is divided into one or more
fractions delivered on a daily basis. Since the imaging, planning,
and delivery stages are performed on different days, patient
anatomy during radiation delivery may differ from that during
imaging stage. This is due to various reasons such as weight loss,
organ motion, tumor shrinkage, etc. In addition, breathing
patterns, physiological changes, and random patient movement during
radiation delivery can also alter the patient anatomy relative to
the radiation beam.
To adapt original treatment plans and/or make inferences about the
success of the plan delivery, clinics have started using feedback
mechanisms (image-based and tracking-based) during treatment. The
feedback mechanisms provide the ability to relate geometric changes
in patient anatomy to the 3D dose distribution received by the
patient. Image-based feedback routines (e.g. cone beam CT (CBCT),
MVCT, B-mode Acquisition, Targeting (BAT) ultrasound, and the like)
are utilized in between fractions and related to the CT simulation
using deformable registration algorithms. Tracking-based methods
(e.g. optical tracking, electromagnetic (EM) tracking, and the
like) are also used during radiation delivery to obtain
intrafraction motion information.
Existing methods for dose estimation/accumulation (e.g. dose
convolution with motion) assume a stationary (time-invariant) plan
dose distribution. In other words, when the spatial position of the
target is related to the plan dose grid, it is assumed that the
dose from all gantry angles and individual beam segments is
delivered simultaneously. This assumption allows the cumulative
dose from all beams (gantry angles) and beam segments to be used in
dose convolution algorithms. This assumption is valid in a
dosimetric sense for stationary and anatomically-invariant targets,
which however, is rarely the case. Additionally, the treatment plan
specifies a finite number of gantry angles (beams) and each beam
has a finite number of segments or control points. These gantry
angles and segments are accessed in a sequential manner.
The present application describes an algorithm of dose verification
that allows the position of the target and/or surrounding normal
tissue to be correlated with the actual dose being delivered at
that instant without the assumption of dosimetric time-invariance
in the dose estimation protocol. Thus, the accuracy of the
estimated dose is improved, since organ motion during delivery is
correlated directly with the dynamic delivered dose and not with
the static plan dose.
The present application provides new and improved methods and
systems which overcome the above-referenced problems and
others.
In accordance with one aspect, a treatment planning system is
provided. The system including one or more processors programmed to
receive a radiation treatment plan (RTP) for irradiating a target
over the course of one or more treatment fractions, said RTP
including a planned dose distribution to be delivered to the
target, receive motion data for at least one of the treatment
fractions of the RTP, receive temporal delivery metric data for at
least one of the treatment fractions of the RTP, calculate a
motion-compensated dose distribution for the target using the
motion data and the temporal delivery metric data to adjust the
planned dose distribution based on the received motion data and
temporal delivery metric data, and compare the motion-compensated
dose distribution to the planned dose distribution.
In accordance with another aspect, a method for generating
patient-specific treatment. The method including receiving a
radiation treatment plan (RTP) for irradiating a target over the
course of one or more treatment fractions, said RTP including a
planned dose distribution for the target an other regions of
interest (for each segment, beam, and the whole), receiving motion
data for at least one of the treatment fractions of the RTP
receiving temporal delivery metric data for at least one of the
treatment fractions of the RTP, calculating a motion-compensated
dose distribution for the target using the motion data and temporal
delivery metric data to adjust the planned dose distribution based
on the received motion data and temporal delivery metric data; and,
comparing the motion-compensated dose distribution to the planned
dose distribution.
One advantage resides in providing improved and more accurate dose
estimation.
Another advantage resides in calculating a radiation dose actually
delivered to a patient.
Another advantage resides in more accurate delivery of radiation to
a target region.
Another advantage resides in more accurately determining a
radiation dose actually delivered to target and non-target
tissue.
Another advantage resides in improvement in radiotherapy treatment
planning workflows used in medical institutions.
Another advantage resides in reducing the risk of normal tissue
damage.
Still further advantages of the present invention will be
appreciated to those of ordinary skill in the art upon reading and
understanding the following detailed description.
The invention may take form in various components and arrangements
of components, and in various steps and arrangements of steps. The
drawings are only for purposes of illustrating the preferred
embodiments and are not to be construed as limiting the
invention.
FIG. 1 is a radiation therapy system in accordance with aspects of
the present disclosure.
FIG. 2 depicts a dose delivery pattern according to aspects of the
present disclosure.
FIG. 3 depicts a method for generating patient-specific treatment
according to aspects of the present disclosure.
FIG. 1 illustrates a radiation therapy system 100 for treating
patients which implements a workflow that provides individual beam
segment-level dose computation and temporal motion tracking for
adaptive treatment planning. Typically, treatment plans for
external beam radiation therapy (EBRT) are usually `static`, i.e.
they are generally developed based on a single computed tomography
(CT) scan. However, the patient's internal anatomy can exhibit
varied motion during radiation delivery, which, if not accounted
for, may lead to significant dosimetric errors. Also, the treatment
plan describes a multi-beam dose distribution that is temporally
invariant. However, radiation delivery is sequential, with each
individual radiation beam delivered for a specific time only. Every
beam also consists of one or more segments, each of which
irradiates a specific region of the target. The workflow, as
described below, improves the accuracy of estimating the effect of
motion on dose, based on high temporal frequency tracking
information on target motion and machine delivery status. The dose
computed from each individual segment of each beam is correlated
with the tracked position of the target, to estimate the dose
actually received by the target and/or surrounding normal tissue.
This information can be used in adaptive treatment
planning/autoplanning workflows.
With reference to FIG. 1, the radiation therapy system is employed
to provide radiation therapy to a patient, such as one or more of
external beam radiation therapy, proton therapy, ablation therapy
and high-intensity focused ultrasound therapy. The radiation
therapy system 100 includes one or more imaging modalities 102
suitable for acquiring images embodying objects of interest (OOIs),
such as regions of interest (ROIs) and points of interest (POIs),
within the patients. The imaging modalities 102 suitably include a
computed tomography (CT) scanner. However, the imaging modalities
102 can additionally or alternatively include one or more of a
positron emission tomography (PET) scanner, a magnetic resonance
(MR) scanner, a single photon emission computed tomography (SPECT)
scanner, and the like.
Images acquired from the imaging modalities 102 are typically
three-dimensional images. However, two-dimensional images are
contemplated. Three-dimensional images typically include a stack of
two-dimensional images, hereafter referred to as slices. Further,
images acquired from the imaging modalities 102 are stored in an
image memory 104. Typically, the image memory 104 is a central
records storage system. However, it is contemplated that the image
memory 104 is local to the imaging modalities 102 or another
component of the radiation therapy system 100. Insofar as the image
memory 104 is remote from the imaging modalities 102, the imaging
modalities 102 are suitably connected therewith via a
communications network, such as a local area network (LAN).
A planning system 106 of the radiation therapy system 100 receives
planning images for each of the patients and employs the images to
generate and/or update radiation therapy treatment plans (RTPs)
and/or to perform post-treatment analysis of RTPs. A planning image
is an image used to generate and/or update an RTP. Typically, the
images are acquired from the image memory 104 and/or the imaging
modalities 102. However, the images can be acquired from other
sources. Further, the planning images are typically received
electronically via a communications network. However, other means
of receiving the planning images are contemplated. Suitably, the
planning system 106 provides typical treatment planning
functionalities, such as manual and automated segmentation tools,
image fusion tools, three-dimensional conformal radiotherapy (CRT)
planning tools, inverse intensity-modulated radiation therapy
(IMRT) optimization tools, dose calculation tools, and so on.
To generate an RTP for a patient, the planning system 106 receives
one or more planning images before radiation therapy. The planning
images are suitably focused on one or more tumors or other targets
of the patient to be treated or observed. Further, the planning
images are suitably three-dimensional and include a plurality of
slices (or two-dimensional images).
Upon receiving the planning images, a contour (or trajectory) is
identified around each of the tumors or other targets and one or
more organs at risk (OARs) or other regions. Contouring is used to
delineate between the tumors or other targets and the OARs or other
regions and between the OARs and the other regions. An oncologist
or other clinician suitably performs the contouring. However,
automated and semi-automated approaches are contemplated. Insofar
as a clinician performs or asserts the contouring, the clinician
suitably employs one or more user input devices 108 to identify the
contours on a graphical user interface presented via a display 110.
For example, the graphical user interface can display a planning
image and allow the clinician to draw or mark the contours on the
planning image using the user input devices 108.
In addition to identifying the contours, radiation plan parameters
are defined for the contoured regions. Suitably, the clinician or
oncologist defines the radiation plan parameters via the graphical
user interface. For example, the clinician defines the radiation
plan parameters using the user input devices 108. However, as with
contouring, automated approaches are contemplated. The radiation
plan parameters typically include minimum or target doses to be
delivered to the tumors or other targets, maximum permissible doses
for the OARs or other regions, and the like.
The radiation therapy plan parameters, together with known
information about radiation attenuation or absorption
characteristics of the various tissues and the contoured tumors or
other targets and the contoured OARs or other regions, are used to
generate the RTP. As discussed below, the RTP defines trajectories
along which the radiation beams irradiate the targets, the
radiation beam spatial projection of each radiation beam
trajectory, the intensity of the radiation beam along each
trajectory, the duration the targets are irradiated along each
trajectory, or the like. In certain embodiments, the RTP is
optimized for the particular type of radiation therapy, such as
external beam radiation therapy, proton therapy, ablation therapy
and high-intensity focused ultrasound therapy.
During each radiation therapy session, the cumulative dose of
radiation delivered to tumors or other targets and OARs or other
regions is determined. As the therapy session progress, the tumors
or other targets typically shrink and the OARs or other regions
typically shift, potentially causing errors in the accumulated dose
calculations and the contours (or trajectories). The RTP and the
integration of cumulative radiation dose delivered to the tumors or
other targets and the OARs or other regions assumes the locations
and sizes of the tumors or other targets and the OARs or other
regions remain as is in the images on which the RTP is based. If
these locations or sizes change, the cumulative radiation doses
will have inaccuracies. Therefore, to maintain accuracy, the RTP is
periodically updated. Although RTPs are typically updated between
treatment fractions, it is contemplated that RTPs are updated
during treatment fractions, other predetermined time periods,
continuously, and the like.
For example, during the radiation therapy session, the dose is
delivered using multiple gantry angles (one at a time). The fluence
emanating from each gantry angle is referred to as a beam. Each
beam consists of multiple segments, which correspond to different
arrangements of the multileaf collimator (MLC) leaf positions. Each
segment may be tailored to irradiate a particular region of the
target. Traditional dose computation schemes cumulatively add the
dose from each segment of every beam together, to arrive at the
final dose distribution. The workflow, as described below, computes
and stores the dose from each segment of every beam separately, in
addition to the cumulative dose grid. Specifically, the dose
distribution may be broken down into its constituent components on
the basis of additional or other factors, such as (but not limited
to) MLC leaf motion patterns, leaf velocities, gantry path, angular
velocity etc.
To update an RTP for a patient, the planning system 106 typically
receives one or more new planning images. For example, the planning
system 106 receives planning images after each, or a predetermined
number of, radiation therapy sessions (or fractions). As above, the
planning images are suitably focused on one or more tumors or other
targets of the patient. Upon receiving a new planning image, or
upon receiving a predetermined number of new planning images, the
contours (or trajectories) and/or the doses of the RTP are
typically updated through comparison of the new planning images to
the planning images used to generate the RTP and/or previous
fractions. Additionally or alternatively, in certain embodiments,
the RTP is updated using a motion/delivery compensated dose module
112 and dosimetric analysis module 114 of the planning system
106.
The motion/delivery compensated dose module 112 calculates doses
actually delivered to a patient (hereafter referred to as
motion-compensated dose distributions) during one or more fractions
of an RTP based on motion data of a patient collected during and/or
between the fractions and the temporal delivery metrics collected
from each beam/segment at different time instants of the radiation
delivery. A motion monitor 118 generates motion data indicative of
motion of the tumors or other targets and/or the OARs or other
regions, relative to previous fractions and/or the RTP. In that
regard, the motion data is typically defined in the coordinate
frame of the planning images employed to generate the previous
fraction and/or the RTP. A dose delivery monitor 120 generates
temporal delivery metric data indicative of the temporal delivery
metrics received from a treatment delivery apparatus, such as a
linear accelerator (LINAC), of a radiation therapy apparatus which
details the status of each beam/segment at each time instant of
radiation delivery. Additionally, the motion/delivery compensated
dose module 112 relates the positional information of the
target/surrounding normal tissue during treatment to the specific
component of the plan dose distribution using the temporal delivery
metric data and utilizes this relationship to calculate the dose
actually delivered to the patient.
The motion data is typically received from one or more surrogates
for the tumors, targets, or other organs (hereafter referred to as
target surrogates). For example, the motion data is received from
three target surrogates disposed at different locations within the
patient. In certain embodiments, the target surrogates are RF
transponders disposed closely adjacent to the target. The motion
monitor 118 in one embodiment includes radio receivers at each of a
plurality of surrounding locations which monitor the signals from
the transponders for phase shifts or other indicators of
displacement and triangulate the location of each transponder. From
the spatial relationship between the transponders and the target,
indicated in the most recent planning images, displacement or a
change in shape of the targets is determined. In other embodiments,
the target surrogates are fiducial markers implanted in the
patient. In one embodiment, the motion monitor 118 includes an
imaging device, such as ultrasounds imaging, projection x-ray
imaging, magnetic resonance imaging (MRI), CT imaging, or the like,
operating, for example, in a fluoroscopic mode. Displacement of the
fiducials is determined by analyzing the fluoroscopic images. In
certain embodiments, target surrogates are not employed. Rather,
image-based motion tracking is employed to receive the motion data.
In one embodiment, the motion monitor 118 includes an imaging
device, as above, that facilitates image-based motion tracking of
the target in real-time using, for example, contours or anatomical
structures.
The motion data can be received continuously, on-demand, upon the
occurrence of an event, such as a timer event, and so on, but is
typically received periodically during radiation therapy, such as
at a frequency of 10 Hz. Where the motion data is received
continuously, it is suitably broken into discrete blocks based on
time and a trending algorithm, such as minimum, median, maximum,
mean, and so on, is applied to the discrete blocks.
The temporal delivery metric data is typically received from the
dose delivery monitor 120 which details the status of each
beam/segment at different time instants of radiation delivery such
as the radiation delivery pattern and sequencing. The temporal
delivery metric data includes the angular position of the gantry at
all times during that fraction in small time increments such as
10-50 ms, although any frequency can be used, the number of the
control points or segments belonging to a particular beam that is
active at any given time instant, and the like. The temporal
delivery metric data can be received continuously, on-demand, upon
the occurrence of an event, and so on, but is typically received
periodically during radiation therapy setup and delivery.
Contemporaneous with or after motion data is collected, the
motion/delivery compensated dose module 112 utilizes the motion
data from the target and/or surrounding normal tissue and the
temporal delivery metric data to calculate motion compensated dose
distributions. Specifically, the motion/delivery compensated dose
module 112 correlates the motion data indicative of motion of the
tumors or other targets and/or the OARs or other regions at each
time step to the temporal delivery metric data indicative of the
temporal delivery metrics received from the treatment delivery
apparatus, such as the LINAC.
In certain embodiments, this includes, for each time step (or
sample) of collected motion data, estimating rigid or deformable
motion of the target surrogates relative to the most recent
planning image used to generate the RTP for the fraction associated
with the time step. For example, suppose motion data for three
target surrogates is collected over the course of two treatment
fractions. A motion estimate is determined for the target and
cuticle tissues relative to the most recent planning image used for
the first fraction. The motion estimate shows the range of
locations over which the target moved and frequency with which the
target was in each location. Rigid motion components include
translations and rotations. Non-rigid motion can also be
employed.
After determining the motion estimates, a cumulative motion pattern
for the tumor or other target during at least a portion of a
fraction is determined. Specifically, each of the motion estimates
corresponding to a given combination of the active segment and beam
are grouped together. Thus, for each active segment belonging to a
particular beam, there is a group of motion estimates that
correspond to the motion that the target underwent at those time
instants. The motion estimates from each are then grouped to all
the target voxels and one or more probability density functions
(PDFs) are created for each of the tumors or other targets based on
the motion estimates for each group. These PDFs represent the
motion patterns of the target during each active segment-beam
combination.
The PDF or other deterioration model is created by applying each of
the motion estimates and corresponding temporal delivery metric
data associated with the treatment fraction to the tumor or other
target to yield a motion-compensated location. The motion of the
tumor or other target and temporal delivery metric data are
accumulated into a PDF to determine the cumulative motion pattern
of the tumor or other target during the fraction. Application of a
motion estimate to a tumor or other target and temporal delivery
metric data shows a portion of the time during irradiation with the
treatment beam that the target was all or partially out of the
treatment beam and which portions were out for how long.
For each of the PDFs, the planned dose distribution corresponding
to the PDF is convolved with the PDF to determine a motion
compensated dose distribution for the fraction(s) corresponding to
the PDF. Specifically, the dose grids are convolved with the
appropriate group of PDFs to generate the motion compensated dose
grid components. The final motion compensated dose distribution is
calculated by adding all the motion-compensated dose grids. The
motion compensated dose distributions for a tumor or other target
can be accumulated until the end of a portion (or subset) of a
fraction, one fraction, or a subset of the fractions.
Alternatively, the motion of each sample or some down samples of
the motion data can be applied directly to the position of the dose
distribution to create a motion compensated dose distribution. The
motion compensated dose distribution can be weighted based on the
amount of time the samples represents and summed to create a
composite motion compensated dose distribution.
The dosimetric analysis module 114 compares the motion compensated
dose distributions of the tumors or other targets to corresponding
planned dose distributions qualitatively or quantitatively.
Typically, but not necessarily, the motion compensated dose
distributions are received from the motion/delivery compensated
dose module 112. In certain embodiments, if significant dosimetric
deviations from the planned dose distributions are detected,
imaging is performed using the imaging modalities 102 as a reality
check on the motion compensated dose distributions.
To qualitatively compare a motion compensated dose distribution of
a tumor or other target with the planned dose distribution, the
motion compensated dose distribution and the planned dose
distribution are graphically displayed on a graphical user
interface presented to an oncologist or other clinician via the
display 110. In certain embodiments, the dose distributions are
displayed adjacent to one another, such as side-by-side. In other
embodiments, the dose distributions are displayed overlaid on one
another with varying transparencies. Suitably, color is employed to
identify dose intensity. For example, a gradient is employed to
identify relative intensity, where the darker the color the greater
the intensity. Further, the contours (or trajectories) can be
overlaid thereon. Using the user input devices 108, the clinician
can sequentially advance through the slices in any dimension (e.g.,
transverse, sagittal, coronal, oblique, etc.) and observe the
resulting two-dimensional dose distributions for a slice. Slices or
projections transverse to one or more therapy beam trajectories are
contemplated. Advantageously, this can help identify obvious and/or
large-scale differences in dose and their spatial locations. In
other words, this can help identify hot spots and/or cold spots. A
hot spot is an area where more radiation than expected was
received, and a cold spot is an area where less radiation than
expected was received. In certain embodiments, the qualitative
comparison further includes receiving comparison data from the user
input devices 108, the comparison data indicating dosimetric
differences between the dose distributions, such as the degree of
similarity of the dose distributions, the location of hot spots
and/or cold spots, and so on.
To quantitatively compare the dose distributions, a number of
different approaches are contemplated. In certain embodiments, a
difference between a planned dose distribution and a motion
compensated dose distribution is calculated. The difference
provides information regarding the presence of cold spots (or hot
spots) in terms of at least one of magnitude, location and extent.
Thresholds can, for example, be applied to the difference to
identify cold spots and/or hot spots. Additionally or
alternatively, in certain embodiments, one or more of dose volume
histograms (DVHs), maximum doses, mean doses, minimum doses, dose
at user specified volumes, etc. of both dose distributions are
compared. Thresholds can, for example, be applied to the comparison
to simplify the identity of clinically significant differences.
Additionally or alternatively, in certain embodiments, the
dosimetric impact of motion is quantified as a weighted combination
of the above factors, with the weights decided by an oncologist or
other clinician.
To perform a post-treatment analysis of an RTP, the planning system
106 receives one or more images after the RTP has completed and/or
motion data, as described above. The images are suitably focused on
one or more tumors or other targets of the patient. Upon receiving
the new images and/or the motion data, at least one of the
motion/delivery compensated dose module 112 and the dosimetric
analysis module 114 is employed to analyze the RTP. The motion
corrected cumulative dose values are determined. The RTP is
adjusted in accordance with the new image, the cumulative dose, the
motion model, and the like. For example, the dosimetric analysis
module 114 can be used to study the effect of motion on the
RTP.
The planning system 106 suitably includes one or more memories 140
and one or more processor-based controllers 142. The memories 140
store executable instructions for controlling a processor of the
processor-based controllers 142 to perform one or more of the above
noted functions of the planning system 106. Further, in certain
embodiments, at least one of the motion/delivery compensated dose
module 112 and the dosimetric analysis module 114 is embodied by
executable instructions stored in, for example, the memories 140.
The processor-based controllers 142 execute the executable
instructions stored on the memories 140 to carry out the functions
associated with the planning system 106. Where the planning system
106 is operative to perform at least one of receive images from a
communications network, store RTPs over a communications network,
and receive motion data from a communications network, the planning
system 106 further includes one or more communications units 144
facilitating communication between the processor-based controllers
142 and the communications networks.
The RTPs generated and/or updated by the planning system 106 are
stored in a radiation therapy plan memory 146. Typically, the
radiation therapy plan memory 146 is the central records storage
system. However, it is contemplated that the radiation therapy plan
memory 146 is local to the planning system 106 or another component
of the radiation therapy system 100. Insofar as the radiation
therapy plan memory 146 is remote from the planning system 106, the
radiation therapy plan memory 146 is suitably connected therewith
via a communications network, such as a local area network
(LAN).
At a scheduled day and time for a radiation therapy session or
fraction of an RTP, a radiation therapy apparatus 148 is employed
to deliver therapeutic radiation to the patient. The radiation can
include gamma rays, particles, x-rays, protons, heat, sound, and so
on suitable for radiation therapy, such as external beam radiation
therapy, proton therapy, ablation therapy and high-intensity
focused ultrasound therapy. Suitably, the radiation therapy
apparatus 148 is controlled by a radiation therapy control system
150 in accordance with the RTP stored in the radiation therapy plan
memory 146. For example, in the illustrated embodiment, the
radiation therapy delivery apparatus 148 includes the linear
accelerator (LINAC), and the radiation therapy control system 150
operates multi-leaf collimator (MLC) or other radiation beam
profile-shaping apparatus of the LINAC to modulate beam intensity
and profile as the linear accelerator is moved or stepped around
the subject, so as to deliver a radiation dose distribution into
the subject that provides the desired integrated radiation dosage
to the target feature while suitably limiting or constraining
radiation exposure of sensitive critical features in accordance
with the RTP.
With reference to FIG. 2, a block diagram of a dose delivery
pattern is provided. The dose delivery pattern 200 includes a
plurality of radiation beams 202, 204, 206. Each of the illustrated
beams 202, 204, 206 represents all parameters pertaining to the
particular beam. Each of the beams 202, 204, 206 consists of one or
more segments 208, 210, 212, 214 and each of the segments has a
dose distribution 216, 218, 220, 222 and motion PDF 224, 226, 228,
230 associated with it. As illustrated, the entire structure is
only elaborated for Beam 2 but it is contemplated that Beam 1 and
Beam `n` would have similar structures. The estimated dose grids
are added together 232 to generate the final motion-contemplated
dose distribution 234.
With reference to FIG. 3, a block diagram of a method 300 performed
by one or more processors to generate patient-specific treatment is
provided. A radiation treatment plan (RTP) for irradiating a target
over the course of one or more treatment fractions is generated or
received 302. The RTP includes a planned dose distribution for the
target. During at least one of the treatment fractions, motion data
is received 304. Additionally, during at least one of the treatment
fractions, temporal delivery metric data is received 306. A
motion-compensated dose distribution for the target is calculated
308 using the motion data and temporal delivery metric data and the
planned dose distribution. The motion-compensated dose distribution
for the target utilizing the motion data and temporal delivery
metric data to adjust the planned dose distribution based on the
received motion data and temporal delivery metric data. Once the
motion-compensated dose distribution is calculated, it is compared
310 to the planned dose distribution.
As used herein, a memory includes one or more of a non-transient
computer readable medium; a magnetic disk or other magnetic storage
medium; an optical disk or other optical storage medium; a random
access memory (RAM), read-only memory (ROM), or other electronic
memory device or chip or set of operatively interconnected chips;
an Internet/Intranet server from which the stored instructions may
be retrieved via the Internet/Intranet or a local area network; or
so forth. Further, as used herein, a processor-based controller
includes one or more of a microprocessor, a microcontroller, a
graphic processing unit (GPU), an application-specific integrated
circuit (ASIC), a field-programmable gate array (FPGA), and the
like; a user input device includes one or more of a mouse, a
keyboard, a touch screen display, one or more buttons, one or more
switches, one or more toggles, and the like; and a display includes
one or more of a LCD display, an LED display, a plasma display, a
projection display, a touch screen display, and the like.
The invention has been described with reference to the preferred
embodiments. Modifications and alterations may occur to others upon
reading and understanding the preceding detailed description. It is
intended that the invention be constructed as including all such
modifications and alterations insofar as they come within the scope
of the appended claims or the equivalents thereof.
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